pathway-enrichment SKILL.md skillA
pathway-enrichment from k-dense-ai/scientific-agent-skills is an agent skill: a SKILL.md folder an AI agent loads when a task calls for it.
mdr add k-dense-ai/scientific-agent-skills/pathway-enrichment@v1.3Pins v1.3 in mdr.lock. Pin the fingerprint instead.
- Audit grade
- A, 17 of 17 checks passed
- Kind
- SKILL.md skill
- Repository
- k-dense-ai/scientific-agent-skills
- Path
skills/pathway-enrichment/SKILL.md- Versions recorded
- 5
- Latest version
- v1.3, committed Oct 1, 2026
- First version recorded
- Jun 11, 2026
- Size
- 16,726 bytes
- Repository stars
- 56,497
- License
- MIT
Performs pathway and gene-set enrichment analysis on gene lists or ranked gene data and interprets the results. Used when the user has a set of genes (differentially expressed genes from PyDESeq2/Scanpy, CRISPR-screen hits, cluster marker genes, proteomics hits) and wants to know which biological pathways, GO terms, or gene sets are over-represented or enriched. Covers over-representation analysis (ORA / Enrichr / Fisher / hypergeometric), ranked Gene Set Enrichment Analysis…
Collected from public GitHub. Every version is saved exactly as it was and never changes. Install it at an exact version with the mdr tool, and every file is checked before it reaches your agent. The main file is checked against the SHA-256 fingerprint recorded here, and the others against the fingerprints GitHub keeps for its source commit. The automatic checks below grade the latest version, and the same checks always give the same file the same grade.
What the file says
# Pathway Enrichment ## Overview Enrichment analysis answers "what biology is over-represented in my genes?" It is the standard last step after differential expression, a screen, or clustering. There are two core methods, and choosing correctly is the single most important decision: - **ORA (over-representation analysis)** — take a *thresholded* gene list (e.g., padj < 0.05) and test which gene sets it overlaps more than chance, using Fisher's exact / hypergeometric tests. Tools: Enrichr, g:Profiler. - **GSEA (gene set enrichment analysis)** — take the *whole ranked list* of genes (no threshold) and test whether each gene set is concentrated toward the top or bottom. Preranked GSEA uses a per-gene score (e.g., the DESeq2 `stat`). Better when effects are broad and subtle. This skill orchestrates these analyses, the gene-set databases behind them, and the interpretation pitfalls that make results wrong or unpublishable. ## When to Use This Skill Use this skill when the user wants to: - Find enriched GO terms / KEGG / Reactome / WikiPathways / MSigDB Hallmark sets in a gene list. - Run GSEA / preranked GSEA on DESeq2, edgeR, limma, or Scanpy `rank_genes_groups` output. …
Read the whole file at its exact version.
How to install
mdr add k-dense-ai/scientific-agent-skills/pathway-enrichment@sha256:82783d8ba63b6b03The line at the top of this page pins the label v1.3, which is what most people want. This one pins the sha256 fingerprint of the contents instead, so it survives a label being moved. Either way the exact version is written to mdr.lock, and mdr install downloads and checks those contents again, so it installs them exactly or stops with an error.
[](https://markdownregistry.com/a/art_v4od42wjuwr7uqsw)
4 badge image fetches in 30 days. What the badge shows.
Versions
The registry has recorded 5 versions of this file, between Jun 11, 2026 and Oct 1, 2026, about one every 28 days.
| version | committed | commit | size | audit | |
|---|---|---|---|---|---|
| v1.3 latest | 2026-10-01 | a92663a | 16,726 B | A | view · diff |
| v1.2 | 2026-09-30 | ce9e6e9 | 13,949 B | A | view · diff |
| v1.1 | 2026-09-02 | 1e5eeff | 13,590 B | A | view · diff |
| v1.0 | 2026-07-26 | b085e11 | 12,632 B | A | view · diff |
| git:20260611.1b8fae3 | 2026-06-11 | 1b8fae3 | 12,637 B | A | view |
Audit of the latest version
- pass: Frontmatter block present
- pass: Frontmatter declares a name
- pass: Frontmatter declares a description
- pass: Size between 200 bytes and 200 KB (16726 bytes)
- pass: No zero-width or bidi control characters
- pass: No instruction hidden inside an HTML comment
- pass: No link to an exfiltration or paste host
- pass: No credential-shaped string
- pass: No instruction to send local credentials anywhere
- pass: No text hidden with inline styles
- pass: No prompt-injection phrasing
- pass: No curl or wget piped into a shell
- pass: No recursive delete of root, home or parent
- pass: No instruction to read or print local credentials
- pass: No base64 blob over 200 characters
- pass: No link to a raw IP address
- pass: No script tag
Source
k-dense-ai/scientific-agent-skills · 56,497 stars · license MIT · pushed 2026-11-02 · branch main
API
GET https://markdownregistry.com/api/v1/artifacts/art_v4od42wjuwr7uqsw GET https://markdownregistry.com/api/v1/resolve?ref=k-dense-ai/scientific-agent-skills/pathway-enrichment GET https://markdownregistry.com/api/v1/blob/82783d8ba63b6b03646492ee75afcdf591903a213decf7e7c9d844eb517acd78
Your AI assistant does the legwork. You hear about the deals worth your word. Hand yours the standing instructions at modelranch.com and it joins the network that reads files like this one.
Guides: the SKILL.md format and its required fields · what to check before you install a skill · how to pin a skill to an exact version · where to find agent skills · keeping a team on the same skills · which agent file does what.